Topic Modeling

install.packages("topicmodels", repos = "http://cran.us.r-project.org")

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install.packages("reshape2", repos = "http://cran.us.r-project.org")

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install.packages("pals", repos = "http://cran.us.r-project.org")

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install.packages("tidytext", repos = "http://cran.us.r-project.org")

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install.packages("dplyr", repos = "http://cran.us.r-project.org")

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library(tm)

## Loading required package: NLP

library(topicmodels)
library(reshape2)
library(ggplot2)

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library(wordcloud)

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library(pals)
library(tidytext)
library(dplyr)

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filename = "https://ximarketing.github.io/class/teachingfiles/lda.csv"
x <- read.csv(filename, header = TRUE)
corpus <- Corpus(DataframeSource(x))
dtm <- DocumentTermMatrix(corpus)

processedCorpus <- tm_map(corpus, content_transformer(tolower))
processedCorpus <- tm_map(processedCorpus, removeWords, stopwords("english"))
processedCorpus <- tm_map(processedCorpus, removePunctuation, preserve_intra_word_dashes = TRUE)
processedCorpus <- tm_map(processedCorpus, removeNumbers)
processedCorpus <- tm_map(processedCorpus, stemDocument, language = "en")
processedCorpus <- tm_map(processedCorpus, stripWhitespace)

minimumFrequency <- 1
DTM <- DocumentTermMatrix(processedCorpus, control = list(bounds = list(global = c(minimumFrequency, Inf))))
# have a look at the number of documents and terms in the matrix
dim(DTM)

## [1] 8134 5015

raw.sum=apply(DTM,1,FUN=sum)
DTM = DTM[raw.sum!=0,]

SEED <- 10112
BURNIN = 1000
ITER = 1000
ntopics = 2
model_lda <- LDA(DTM, k = ntopics, method = "Gibbs", control = list(seed = SEED, burnin = BURNIN, iter = ITER))

terms(model_lda, 10)

##       Topic 1      Topic 2    
##  [1,] "servic"     "clinic"   
##  [2,] "use"        "onlin"    
##  [3,] "quick"      "medic"    
##  [4,] "recommend"  "time"     
##  [5,] "deliveri"   "day"      
##  [6,] "excel"      "get"      
##  [7,] "fast"       "need"     
##  [8,] "profession" "doctor"   
##  [9,] "easi"       "prescript"
## [10,] "order"      "appoint"

topics <- tidy(model_lda, matrix = "beta")
top_terms <- topics %>%
  group_by(topic) %>%
  top_n(10, beta) %>%
  ungroup() %>%
  arrange(topic, -beta)

top_terms %>%
  mutate(term = reorder_within(term, beta, topic)) %>%
  ggplot(aes(beta, term, fill = factor(topic))) +
  geom_col(show.legend = FALSE) +
  facet_wrap(~ topic, scales = "free") +
  scale_y_reordered()